scikit-learnA set of python modules for machine learning and data mining.. -*- mode: rst -*-
|Travis|_
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scikit-learn
============
scikit-learn is a Python module for machine learning built on top of
SciPy and distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the AUTHORS.rst file for a complete list of contributors.
It is currently maintained by a team of volunteers.
**Note** `scikit-learn` was previously referred to as `scikits.learn`.
Important links
===============
- Official source code repo: https://github.com/scikit-learn/scikit-learn
- HTML documentation (stable release): http://scikit-learn.org
- HTML documentation (development version): http://scikit-learn.org/dev/
- Download releases: http://sourceforge.net/projects/scikit-learn/files/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
- Mailing list: https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
- IRC channel: ``#scikit-learn`` at ``irc.freenode.net``
Dependencies
============
The required dependencies to build the software are Python >= 2.6,
setuptools, Numpy >= 1.3, SciPy >= 0.7 and a working C/C++ compiler.
For running the examples Matplotlib >= 0.99.1 is required and for running the
tests you need nose >= 0.10.
This configuration matches the Ubuntu 10.04 LTS release from April 2010.
Install
=======
This package uses distutils, which is the default way of installing
python modules. To install in your home directory, use::
python setup.py install --home
To install for all users on Unix/Linux::
python setup.py build
sudo python setup.py install
Development
===========
Code
----
GIT
~~~
You can check the latest sources with the command::
git clone git://github.com/scikit-learn/scikit-learn.git
or if you have write privileges::
git clone git@github.com:scikit-learn/scikit-learn.git
Testing
-------
After installation, you can launch the test suite from outside the
source directory (you will need to have nosetests installed)::
$ nosetests --exe sklearn
See the web page http://scikit-learn.org/stable/install.html#testing
for more information.
Random number generation can be controlled during testing by setting
the ``SKLEARN_SEED`` environment variable.http://sourceforge.net/projects/scikit-learn/files/Andreas Muellerd86db7a2f06600abbf320801584499fd21e0c1ef0.13